Artificial Intelligence and Machine Learning have the potential to revolutionize DevOps productivity and business efficiency. DevOps model symbolizes as a mean to accelerate development efforts and deliver new applications. Ability to build applications with the latest production data, deliver updates quickly with more application testing in less time, speeding up the release cycle and integration testing and reducing the restoration time are the main requirements for DevOps. DevOps practices results in large amount of data and to analyze such huge data technologies like Machine Learning and Artificial Intelligence are required.
Artificial Intelligence and Machine Learning are providing solutions to optimize the DevOps processes. It helps to keep track of production performance, establish links to previous problems and how effective were the solutions that was provided. AL mad Ml can perform automating routine and repeatable actions of DevOps with enhanced efficiency to improve the performance of teams and business.
AI has huge impact on DevOps in the following ways:
Improves data accessibility: AI can help DevOps team in collecting data from multiple sources and preparing it for reliable and robust evaluation. Artificial Intelligence resolves the lack of unstructured accessibility to data by releasing data from its formal storage which is the major issue in DevOps team.
Efficient implementation capability: AI allows teams to switch from rules-based human management system to self-governed systems. Thus improving the efficiency by resolving the complexity of assessing human agents.
Adequate utilization of resources: AI helps in minimizes the complexity of managing resources since AI has the capability of automating routine and repeatable tasks.
Ways in ML can optimize DevOps:
Analyzing and tracking application delivery: Application of ML on DevOps tools identify the irregularities in the long development duration, slow release-rates to uncover software development wastes, inefficient resourcing and slow down processes.
Securing application quality: ML can efficiently build a test pattern by analyzing the output from testing tools. It can review QA results and ensure testing on every release and increasing the quality of delivered applications.
Managing productions: ML helps in analyzing the general patterns and detect irregular patterns like memory leaks and race conditions. This technology helps in evaluating the large amount of data occurred during production.
Predicting application and server failure: Depending on the previous data, Machine Learning can be used employed to predict future server and application failure. Thus it helps in reloading the servers before failure occurs and preventing downtime.
Log analysis: Analyzing logs enables machines to identify patterns and make decisions. This can also be used for security and performance.
Organizations can employ AI and ML to optimize their DevOps environment. These technologies helps in managing complex data and thereby utilizing it in the app development process. Applications of AI and ML can result in ROI of a company by enhancing DevOps operations and making it more responsive. They can improve efficiency as well as productivity of the team. It helps DevOps teams to focus on creativity and innovation by eliminating the issues in the processes and enabling teams to manage the available data.